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\title{General Subsequence Matching Framework}
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\begin{abstract}
Subsequence matching has appeared to be a best approach to solve many problems related to the fields of data mining and similarity retrieval. It has been shown that almost any data class (audio, image, biometrics, signals) is or can be represented by some kind of timeseries or string which can be seen as an input for various subsequence matching approaches. However, the previous research in this field has suffered from problems like data and implementation bias so it was not an easy task to properly compare competing approaches. Therefore we present a new subsequence matching framework that speeds up a development of subsequence matching related systems and helps to overcome the mentioned biases by providing unified testing environment. Furthermore, we show on prototypes that this framework can be used in many application domains and its components can be reused effectively.
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\section{Introduction}
Definition of subsequence matching problem\newline
Lots of data transferable to timeseries (audio, gait, biometrics, pictures)\newline
Lots of common problems\newline
Current problems with timeseries subsequence matching (natural timeseries)\newline
Implementation and Data Bias\newline
Need for flexibility in combining methods for subseq matching\newline
Aiming for performance and efficiency experiments (fast prototyping, testing environment)\newline

\section{Framework}

\subsection{Subsequence matching overview}
Short overview of subseq matching and the last fifteen years of research(data representaion, data reduction, indexing, lowerbounding, matching strategies, distances)\newline
\subsection{Common Sub-problems}
Modules for common problems (motivation, isolation of modules, examples)\newline
Module functionality is independent of data or other modules which allows easy combination\newline
\subsubsection{Data Representation and Dimensionality Reduction}
Known significant solutions (Classics in signal processing DFT, PAA; MFCC for audio, SIFT for images)
\subsubsection{Windows}
Known significant solutions (sliding, disjoin, dual approach)
\subsubsection{Indexing}
Known significant solutions (metric indexes, iSAX)
\subsubsection{Distance Functions}
Known significant solutions (ED, warping, DTW, Edit Distance based)

\subsection{Subsequence Matching Strategies and the Framework}
Algorithms for subseq matching, using modules, data independent, each algorithm solves class of problems \newline
Combining modules to tune performance and finding best solution for the particular data
Possible queries (including approx)

\section{Implementation}
Java, MESSIF, MUFIN, Config files scripting, Web and Mobile Apps\newline

\section{Use Cases}
General timeseries\newline
Gait recognition\newline
Query-by-example spoken term detection\newline

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